The methods and systems illustrated herein in embodiments are related generally to the art of halftoning images. More specifically, embodiments are related to error diffusion (ED), rank ordered error diffusion (ROED) and issues related to quantization decisions. Embodiments find particular application where a halftoned image is scanned into an image processing system for the purpose of further processing or copying. However, embodiments are also beneficially applied wherever there is a desire to adjust image contrast. For example, embodiments may be applied to images from sources including, but not limited to, Adobe™ gray tile, JPEG uncompressed images, scanned line art and anti-aliased images (Adobe is a registered trademark of Adobe Systems Inc.).
Binary halftone images consist of a multitude of tiny marked spots on an unmarked background. The spots are laid out in a grid. For example, the spots are laid out with the structure of a halftone screen. When a halftone image is scanned, for example, during a copying procedure, it is highly unlikely that the locations of the marked and unmarked portions of the image exactly coincide with the locations of sensors in the scanning device. Therefore, a marked spot may only be partially in the field of view of a related image sensor. For this and other reasons, scanned halftone images tend to include gray edges around the halftone spots or dots. To print such grayed halftones, or grayed halftones from other sources, using a binary printer it can be necessary to convert the gray level values of the scanned image into a binary (mark or unmark) form, or to correct or remove them.
Several methods for converting grayed images into a binary form are known. For example, grayed images can be binarized through thresholding, rehalftoning and through a variety of error diffusion (ED) techniques.
Many error diffusion techniques are known. For example, the Proceedings of the Society for Information Display, Volume 17, 1976, include a discussion of screening algorithm called the Floyd and Steinberg error diffusion algorithm; Billotet-Hoffman and Bryngdahl in the Proceedings of the Society for Information Display, Volume 24, 1983, discuss modifications to the Floyd-Steinberg algorithm which include a varying threshold, a dither, instead of a fixed threshold; and U.S. Pat. No. 5,045,952 to Eschbach entitled METHOD FOR EDGE ENHANCED ERROR DIFFUSION; U.S. Pat. No. 5,208,871 to Eschbach entitled PIXEL QUANTIZATION WITH ADAPTIVE ERROR DIFFUSION; U.S. Pat. No. 5,226,096 to Fan entitled DIGITAL HALFTONING WITH SELECTIVELY APPLIED DOT-TO-DOT ERROR DIFFUSION; U.S. Pat. No. 5,321,525 to Hanes entitled CLUSTERED HALFTONING WITH DOT-TO-DOT ERROR DIFFUSION and U.S. Pat. No. 5,268,774 to Eschbach entitled HALFTONING WITH ENHANCED DYNAMIC RANGE AND EDGE ENHANCED ERROR DIFFUSION, all of which are totally incorporated herein by reference, describe various embodiments of error diffusion (ED).
In some instances, there are side effects associated with processing a gray halftone image through conventional threshold processing methods or error diffusion (ED). For example, simple, uniform (same threshold applied everywhere) thresholding can remove intermediate gray levels and, therefore, can introduce an unacceptably large gray error. In rehalftoning, frequency components of the new halftone screen can combine undesirably with a halftone grid pattern of the original or scanned image to produce objectionable moiré patterns. Conventional error diffusion (ED) can usually render a scanned halftone without pattern artifacts. However, conventional error diffusion (ED) techniques often produce images with excessive fragmentation of dots. In at least some environments, such as, for example, some xerographic environments, dot or spot fragmentation is to be avoided. Compact halftone dots are more forgiving of non-linearities and process drifts associated with reprographic devices than are tiny dot fragments associated with a diffuse fragmentary halftone dot. For example, a small dimensional offset in the size of a tiny dot fragment represents a larger dot gain error than does the same dimensional offset applied to a large compact dot. Dot gain errors are perceived as errors in the lightness or darkness of an image or portions of an image.
Rank ordered error diffusion (ROED) is a form of error diffusion that strives to produce compact dots. U.S. Patent Application Publication No. U.S. 2003/0090729 A1 by Loce, et al., filed Oct. 1, 2001 and published on May 15, 2003 entitled RANK ORDER ERROR DIFFUSION IMAGE PROCESSING, the disclosure of which is totally incorporated herein by reference, explains that in rank ordered error diffusion, error is distributed to pixels neighboring a pixel of interest (or target pixel) based on a ranking of the neighboring pixels. The ranking is based on pixel values of the neighboring pixels. Optionally, a spatial weighting is applied to the pixel values before ranking to provide a preference for pixels closest to the target pixel or to a particular portion of a related halftone screen.
While rank ordered error diffusion (ROED) improves dot clustering, in some circumstances, images produced through ROED may be perceived to include objectionable moiré. As indicated above, standard or non-rank ordered error diffusion (ED) techniques tend to produce images that are perceived to be moiré free.
Therefore, there is a desire for an image halftoning algorithm that provides the best compromise between the moiré resistance of ED and the low noise or dot clustering of ROED.
Increasing image resolution is another mechanism for improving rendered image quality. Recently, techniques have been developed for controlling the marking process of marking engines at subpixel resolutions, at least in a cross-process direction. Such subdivided pixels are said to have high addressability or to be high addressable. Pixel quantization processes have been adapted to take advantage of high addressability. Whereas standard pixels are associated with a binary quantization (0 or 1, mark or no mark), high addressable pixels are associated with a multi-level quantization. For example, where a pixel can be subdivided into four subpixels, a contone value of a pixel may be quantized to one of five quantization levels (e.g., 0, 1, 2, 3 and 4, associated with contone values of, for example, 0, 64, 128, 192 and 255). The selected quantization level identifies how many of the subpixels are to be marked.
U.S. Pat. No. 5,325,216 to AuYeung entitled RASTER OUTPUT SCANNER WITH SUBPIXEL ADDRESSABILITY; U.S. Pat. No. 6,301,397 B1 to Jankowski, et al. entitled SYSTEMS AND METHODS FOR ROTATING HIGH ADDRESSABILITY IMAGES and U.S. Pat. No. 6,449,396 B1 to Loce, et al. entitled COMPACT RENDERING BINARY HIGH ADDRESSABILITY IMAGES, the disclosures of which are totally incorporated herein by reference, disclose aspects of high addressability processing. U.S. Pat. No. 5,374,997 to Eschbach entitled HIGH ADDRESSABILITY ERROR DIFFUSION WITH MINIMUM MARK SIZE; U.S. Pat. No. 5,528,384 to Metcalfe, et al. entitled SYSTEM AND METHOD FOR IMPLEMENTING FAST HIGH ADDRESSABILTY ERROR DIFFUSION PROCESS; U.S. Pat. No. 5,608,821 to Metcalfe, et al. entitled METHOD OF HIGH ADDRESSABILITY ERROR DIFFUSION; U.S. Pat. No. 6,351,319 B1 to Schweid, et al. entitled SYSTEM AND APPARATUS FOR SINGLE SUBPIXEL ELIMINATION WITH LOCAL ERROR COMPENSATION IN A HIGH ADDRESSABLE ERROR DIFFUSION PROCESS and U.S. Pat. No. 6,353,687 B1 to Schweid also entitled SYSTEM AND APPARATUS FOR SINGLE SUBPIXEL ELIMINATION WITH LOCAL ERROR COMPENSATION IN A HIGH ADDRESSABLE ERROR DIFFUSION PROCESS, the disclosures of which are totally incorporated herein by reference, discuss high addressability in the context of error diffusion. U.S. Pat. No. 5,504,462 attributed to Clanciosi (a misspelling of Cianciosi), et al. entitled APPARATUS FOR ENHANCING PIXEL ADDRESSABILITY IN A PULSE WIDTH AND POSITION MODULATED SYSTEM and U.S. Pat. No. 6,184,916 B1 to Cianciosi entitled MULTIPLE-PULSE, PULSE WIDTH AND POSITOIN MODULATION SYSTEMS AND METHODS, the disclosures of which are totally incorporated herein by reference discuss high addressability in terms of pulse width and positioned modulation implementations.
Taking advantage of an availability of high addressability can improve rendered image quality. However, taking advantage of high addressability also increases demands on image processing resources and consumes communication bandwidth.
Therefore, there has been a desire for systems and methods that can take advantage of high addressability when it would be most beneficial and select low addressability or standard binary pixel processing in portions of an image where increased resolution would be of little benefit. Furthermore, there has been a desire to combine the benefits of such adaptive quantization with benefits of a halftoning algorithm that provided the best compromise between the moiré resistance of ED and the low noise or dot clustering of ROED.